**Algorithms are the smart and powerful soldier of a complex machine learning model. In other words, machine learning algorithms are the core foundation when we play with data or when it’s come to training the model.**

In this article, you and I are going on a tour called ”7* major machine learning algorithms and their application*”.

The purpose of this tour is to either brush up the mind and build a more clear understanding of the subject or for beginners provide an essential understanding of machine learning algorithm.

We will find the major answer in this tour like for what purpose machine learning algorithms works, where to use them, when to use them and how to use them.

Before getting deeper let’s have a brief introduction about machine learning algorithm classification. Machine learning algorithms are mainly classified into 3 broad categories i.e supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning machine learning algorithms, the machine is taught by example. Here the operator provides the machine learning algorithm with the dataset. This dataset includes the desired inputs and outputs variables.

By the use of these set of variables, we generate a function that map inputs to desired outputs. After that the machine learning algorithm starts to find a method to determine how to arrive at those inputs and outputs, the operator knows the correct answers to the problem.

The algorithm recognizes the patterns in data, learn from observations and finally makes predictions. The predictions made by the algorithm is corrected by the operator and this process continues until the algorithm achieves the desired level of accuracy/performance on the training data.

The supervised learning is mostly found useful when a property or label is available for a certain dataset.

Further supervised learning is classified into Classification, Regression, and Forecasting. Machine learning algorithms like Decision Tree, Random Forest, KNN, Logistic Regression, etc are the type of supervised learning.

If we talk about unsupervised learning, then in this type of machine learning algorithms the machine study data to identify patterns. In unsupervised learning, there are only input variables (X) but no corresponding output variables.

Here the machine learning algorithm interprets large data sets and tries to organize that data in some way to describe its structure, this might mean grouping the data into clusters or arranging it in a way that looks more organized.

Unsupervised learning machine learning algorithms are used unlabeled training data to model the underlying structure of the data.

Machine learning algorithms belong to unsupervised learning such as Apriori algorithm and K-mean are very useful in cases where the challenge is to discover implicit relationships in a given unlabeled dataset i.e where the items are not pre-assigned.

For example clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.

Now the last category of the machine learning algorithm is reinforcement learning, the reinforcement learning is a type of machine learning algorithm that helps to decide the best next action based on its current state, from learning behaviors that will maximize the reward.

The reinforcement learning focuses on regimented learning processes, where a machine learning algorithm is provided with a set of actions, parameters and end values. After defining the rules the machine learning algorithm then tries to explore different options and possibilities, monitoring and evaluating each result to determine which one is optimal.

The reinforcement learning learns from past experiences and begins to adapt its approach in response to the situation to achieve the best possible result, in other words, it teaches the machine through trial and error.

In reinforcement learning for each predictive step or action, there is some form of feedback available but there is no precise label or error message in reinforcement learning.

Reinforcement algorithms are usually used in robotics where a robot can learn to avoid collisions by receiving negative feedback after bumping into obstacles, and in video games where trial and error reveals specific movements that can shoot up a player’s rewards.

You can implement all these kind of algorithms in any language you prefer, but machine learning algorithm in python is the identity of genius and the one who care about time.

Now after getting a general introduction about the machine learning algorithms types, let us dive into our 10 major machine learning algorithms list and eat them out.

**1. Naive Bayes**

Navie Bayes is a machine learning algorithm that is particularly based on Bayes’ theorem with an assumption of independence between predictors. It is one of a simple machine learning algorithm that brings lots of powerful on the table and it is also best suited for predictive modeling.

Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data, nevertheless, the technique is very effective on a large range of complex problems.

Bayes theorem is a way to find out the conditional probability, the conditional probability is a probability of an event happening given that it has some relationship to one more other events.

For example, your probability of getting a parking space is connected to the time of the day you parked, where you park and what conventions are you going take on that time.

By the use of this machine learning algorithms, we will be dealing with the probability distributions of the variables in the dataset, predicting the probability of the response variable belonging to a particular value, given the attributes of a new instance.

In Naive Bayes, the classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Its model is comprised of two types of probabilities that can be calculated directly from your training data:

1) The probability of each class; and 2) The conditional probability for each class given each x value.

In machine learning, we many times select the best hypothesis (c) given data (x). In a classification problem, our hypothesis (c) may be the class to assign for a new data instance (x). Bayes’ Theorem provides a way that we can calculate the probability of a hypothesis given our prior knowledge.

Bayes’ Theorem is stated as:

where:

- P(c|x) = This is called the posterior probability. The probability of hypothesis h being true, given the data d, where P(c|x)= P(x1| c) P(x2| c)….P(xn| c) P(d)
- P(x|c) = This is called the Likelihood. The probability of data d given that the hypothesis h was true.
- P(c) = This is called the Class prior probability. The probability of hypothesis h being true (irrespective of the data)
- P(x) = This is is called the Predictor prior probability. The probability of the data (irrespective of the hypothesis)

After calculating the posterior probability for a number of different hypotheses, you can select the hypothesis with the highest probability. This is the maximum probable hypothesis and may formally be called the maximum a posteriori (MAP) hypothesis.

The calculated probability model can also be used to make predictions for new data using Bayes Theorem. When your data is real-valued it is common to assume a Gaussian distribution (bell curve) so that you can easily estimate these probabilities.

The complexity of the above Bayesian classifier needs to be reduced, for it to be practical. The Naive Bayes algorithm does that by making an assumption of conditional independence over the training dataset. This drastically reduces the complexity of the problem.

Model-based on Naive Bayesian machine learning algorithms are easy to build and particularly useful for very large data sets and very effective on a large range of complex problems. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

This kind of machine learning algorithms has lots of different application such as categorizing news, email spam detection, face recognition, sentiment analysis, medical diagnosis, digit recognition, and weather prediction.

If we talk about the machine learning algorithm example Naive Bayes is one of most beautiful.

If you want to explore more about Naive Bayes then here is an amazing detail-oriented article “Naive Bayesian Model” from Abhay Kumar our lead Data Scientist.

**2. Decision Tree**

Decision Tree is one of the most known machines learning algorithms. It is a tree-like flow-chart structure that is used to visually and explicitly represent decisions and to illustrate every possible outcome of a decision.

It is a graphical representation of possible solutions to a decision based on certain conditions. It’s called a decision tree because it starts with a single box (or root), which then branches off into a number of solutions, just like a tree.

The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or final outcomes and each node within the tree represents a test on a specific variable. And the decision nodes are where the data is split.

The Decision Tree algorithm is s a supervised learning algorithm that works for both categorical and continuous dependent variables.

A decision tree is drawn upside down with its root at the top. In the image on the left, the bold text in black represents a condition/internal node, based on which the tree splits into branches/ edges.

The end of the branch that doesn’t split anymore is the decision/leaf, in this case, whether the passenger died or survived, represented as red and green text respectively.

The Decision Tree machine learning algorithm is a type of supervised learning algorithm that is mostly used for classification problems.

There are two main types of Decision Trees, Classification trees (Yes/No types), Regression trees (Continuous data types).

Tree models where the target variable can take a discrete set of values are called classification trees, in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.

Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.

The assumptions we make while using these machine learning algorithms are that at the beginning, the whole training set is considered as the root. Feature values are preferred to be categorical. If the values are continuous then they are discretized prior to building the model.

Records are distributed recursively on the basis of attribute values. Order to placing attributes as root or internal node of the tree is done by using some statistical approach.

If we talk about the application of Decision trees then there is numerous area where we use decision trees such as predicting and reducing customer churn across many industries, fraud detection in the insurance sector, credit risk scoring in the banking and financial services.

If you looking for some machine learning algorithm books then I have three for you that will help you digest this first two and other algorithms quicks, Understanding Machine Learning: From Theory to Algorithms, Machine Learning for Dummies, Machine Learning Yearning.

**3. Linear Regression**

Linear regression is one of the most known machines learning algorithms and it is a very simple approach to supervised learning.

Linear regression is the most basic type of regression. It was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but has been borrowed by machine learning.

Linear regression is a linear model, example a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, y can be calculated from a linear combination of the input variables (x).

In machine learning, we have a set of input variables (x) which are used to determine the output variable (y). A relationship exists between the input variables and the output variable. The goal of ML is to quantify this relationship.

Whenever there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression.

For understanding the working functionality of linear regression, let’s imagine how you would arrange random logs of wood in increasing order of their weight.

There is a catch, however – you cannot actually weigh each log. You have to guess its weight just by looking at the height and girth of the log (visual analysis) and arrange them using a combination of these visible parameters. This is what linear regression is like.

Mathematically, we can write a linear relationship as:

Where:

*1) \(y\) *is the response

*2) \(β\)* values are called the model coefficients. These values are “learned” during the model fitting/training step.

*3) \(β0 \)* is the intercept

*4) \(β1\)* is the coefficient for *X1* (the first feature)

*5) \(βn\)* is the coefficient for *Xn *(the nth feature)

There are different techniques that we can use to learn the linear regression model from data, such as a linear algebra solution for ordinary least squares and gradient descent optimization.

Linear regression has been around for more than 200 years and has been extensively studied.

Some good rules of thumb when using this technique are to remove variables that are very similar (correlated) and to remove noise from your data, if possible. It is a fast and simple technique and good first algorithm to try.

If you want to know more about linear regression in detail you can head over to Jason Brownlee articles called “Linear Regression for Machine Learning”. Jason Brownlee, Ph.D. is a machine learning specialist.

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